As illustrated in FIG. 1, magnetic flux 10 travels between the poles of a magnetic field along a path of least reluctivity (i.e., highest permeability). When a ferromagnetic material 15 is present within a magnetic field, the magnetic flux 10 concentrates within the ferromagnetic material 15, which has a magnetic permeability that is much higher than non-ferromagnetic materials. However, the flux capacity of a ferromagnetic material 15 is proportional to its cross-sectional area. Therefore, when the cross-sectional area of a flux-carrying ferromagnetic material 15 is reduced, magnetic flux “leaks” 20 into the surrounding medium. This flux leakage 20 occurs on both sides of the ferromagnetic material 15.
Magnetic flux leakage (MFL) tools take advantage of this phenomenon to identify and evaluate defects in oil and gas well casings. MFL tools induce a magnetic flux within the ferromagnetic (e.g., steel) wall of a casing and measure any changes in the magnetic flux that “leaks” from inside the casing wall. The MFL tool 100 illustrated in FIG. 2 includes a strong permanent magnet 105 (such as a Samarium-Cobalt magnet) that induces a magnetic flux that travels along the longitudinal axis of a wellbore, primarily within the wall of the casing. The MFL tool 100 additionally includes a plurality of pads 110 that are positioned around the circumference of the tool 100, each pad 110 biased towards the interior wall of the casing. Each pad includes one or more corrosion sensors that measure magnetic flux in the longitudinal direction (i.e., the flux generated as a result of the permanent magnet). Each pad 110 also includes one or more discriminator sensors that measure magnetic flux in a direction orthogonal to the longitudinal direction, which flux is induced by one or more smaller permanent magnets also positioned on the pad 110. The flux leakage measured by the discriminator sensors is more sensitive to defects in the interior wall of the casing. The MFL tool 100 additionally includes an upper centralizer 115 and a lower centralizer 120 that keep the MFL tool 100 positioned in the center of the casing.
The MFL tool 100 is specific to a particular range of casing sizes, and the number of pads 110 and corrosion sensors is dependent upon the specific tool. By way of example, the tool 100 may include between 10 and 16 pads and between 30 and 80 corrosion sensors, which corrosion sensors may be arranged in groups of three to five sensors per pad. These types of tools may service API casing having an external diameter between 4½ and 9⅝ inches. It will be understood that different MFL tools may include different numbers of pads and sensors and may service different casing sizes.
The corrosion and discriminator sensors generate an electrical signal that is proportional to the amount of flux traveling through the sensor in the selected direction (i.e., the longitudinal direction for corrosion sensors and orthogonal to the longitudinal direction for discriminator sensors). The sensors are typically Hall Effect sensors, but they can also be coil-type sensors. The electrical signal at each of the sensors is recorded periodically as the tool travels in the wellbore. Recording of the signals may be accomplished by storing digitized values of the signals in a memory contained within the tool 100 or by transmitting the values to a system at the surface such as via a wireline that conveys the tool 100 into the wellbore. The MFL tool 100 may be logged at a rate of 6000 feet per hour, and, at that rate, the signal from each of the tool's sensors may be recorded at a rate of 120 samples per foot, although other conveyance and recording rates may also be used.
As illustrated in the cross-sectional schematic view in FIG. 3, the magnetic flux induced by the permanent magnet 105 in the tool travels primarily within the wall of the casing. The magnet 105 is typically sized for the specific type of casing such that the field strength results in near magnetic saturation of the casing wall 150. As shown in FIG. 3, magnetic flux leakage occurs on both sides of the casing wall 150 in the area of both internal defects 155 and external defects 160. The corrosion sensors, which are positioned on the pads 110 riding along the interior wall of the casing 150, will therefore measure increased magnetic flux in the area of either an internal defect 155 or an external defect 160. Because the magnets associated with the discriminator sensors are not strong enough to magnetize the full thickness of the wall 150, the discriminator sensors exhibit a greater response to internal defects 155. As such, the response measured by a corrosion sensor can be classified as corresponding to either an internal defect 155 or an external defect 160 based on the response of a closely-positioned discriminator sensor.
Signals representative of magnetic flux are recorded from sensors positioned at numerous azimuthal positions at each of numerous longitudinal positions within the cased wellbore. These recorded signals can be compiled into a magnetic flux leakage image that provides a visualization of the features of the evaluated casing. FIG. 4 shows a cutaway view of section of a casing string 405 that includes a coupling 410 and an example of the MFL image 440 associated with the section of the casing string 405. The signals recorded from each of the corrosion sensors in regions 420 of defect-free casing 415 are substantially equal and correspond to the nominal flux for the casing type. In the regions 425 near the outer edges of the coupling 410, the signals recorded from each of the corrosion sensors are also substantially equal, but they correspond to a magnetic flux that is greater than the nominal flux. The increased flux leakage that is detected at all azimuthal positions in the regions 425 is due to the incomplete engagement of the casing's threads with the coupling's threads, which results in a decreased wall thickness at the initiation of the casing threads. In the region 430 corresponding to the coupling 410, the signals recorded from each of the corrosion sensors are still substantially equal, but they correspond to a magnetic flux that is less than the nominal flux. The decreased flux leakage that is detected at all azimuthal positions in this region is due to the increased wall thickness of the coupling 410 as compared to the wall thickness of the casing 415, which results in the additional concentration of magnetic flux within the wall of the coupling 410 and therefore decreased magnetic flux measured by the corrosion sensors.
The signals recorded by the MFL tool's sensors can also be utilized to visualize defects in the casing wall. The MFL image 505 in FIG. 5 visually depicts the magnetic flux measured in a region of casing having a defect. The corrosion sensors at azimuthal positions near the defect measure increased magnetic flux leakage as a result of the decreased wall thickness caused by the defect while corrosion sensors at other azimuthal positions measure magnetic flux corresponding to the nominal flux value. The image 505 enables a determination of the longitudinal and azimuthal location of a defect, but the magnetic flux signals recorded by the tool 100 can additionally be utilized to evaluate the degree of penetration of the defect and the burst pressure of the casing as a result of the defect.
FIG. 6 illustrates an existing process 600 for determining the degree of penetration of a defect within an evaluated casing string and the casing string's burst pressure as a result of the defect. The MFL signals are acquired at step 605 as described above. The MFL signals may be acquired over a range of longitudinal positions within which there is believed to be a casing defect or may be acquired over a substantial length of the casing string as part of a routine analysis. As noted above, the MFL signals may be transmitted to the surface as they are acquired or they may be stored by the tool 100 and the recorded signals may be recovered when the tool 100 is subsequently brought to the surface. The acquired signals are then aligned to consistent longitudinal positions (step 610). Note from FIGS. 2 and 3 that the pads 110 are at different longitudinal positions. Moreover, the corrosion and discriminator sensors on a particular pad may also be at different longitudinal positions. Thus, signals acquired by the various sensors at the same point in time correspond to different longitudinal positions. At step 610, the signals are adjusted to a consistent longitudinal scale. The longitudinal alignment process arranges the recorded MFL signals into a matrix that defines an MFL image, where each value represents a single pixel in the image. Each column in the matrix includes MFL signals recorded by a single corrosion sensor at different longitudinal positions and each row in the matrix includes MFL signals recorded by the different corrosion sensors at a common longitudinal position.
At step 615, the aligned signals (i.e., the signals that make up the image) are evaluated to identify a casing defect through a quantitative analysis of the signals. A defect may be identified on the basis of the magnitude of the raw MFL signals or on the basis of some other quantitative metric such as a first or second order derivative of the MFL signals with respect to longitudinal position, for example. The identification of a defect may also involve the identification of a longitudinal range within which the defect is to be further evaluated. This, too, may be based upon a quantitative analysis of the MFL signals. Defect identification can also involve discarding regions of increased magnetic flux that do not correspond to a defect, such as the regions 425 in FIG. 4. This process can be performed through quantitative and/or qualitative analysis. At the defect identification stage, the defect is also classified as either internal or external. As described above, this determination is based upon the signals recorded by the discriminator sensors within the region of the defect.
Having identified a defect, a characteristic value of the defect is computed (step 620). Like the identification of a defect, the characteristic value of the defect can also be determined quantitatively from the MFL signal values. For example, the characteristic value of the defect may be the maximum raw value within the determined longitudinal range. In a particular example, the characteristic value is the maximum value of the second derivative with a selected longitudinal window length (i.e., a particular longitudinal distance over which signal change is evaluated) of the MFL signal values with respect to longitudinal position within the determined longitudinal range. The characteristic value can also be computed using other metrics that are relatable to the degree of penetration of the defect.
The degree of penetration of the defect is determined based on the characteristic value of the defect and the defect's classification as either internal or external (step 625). FIG. 7 shows an example characteristic value-degree of penetration relationship 700 (hereinafter “relationship”). This type of relationship is developed experimentally by determining the characteristic value for known defects having different degrees of penetration and different classifications (i.e., internal or external origination). For example, the characteristic value may be determined for defects having degrees of penetration between 5% and 100% of the casing wall thickness in 5% increments for both internal and external detects. The relationship is specific to a particular type of MFL tool and a particular type of casing because different types of tools record different MFL signals for the same casing defect and different types of casing (i.e., different sizes, grades and wall thicknesses) result in different characteristic values for the same defect. The relationship is also based upon an assumed defect geometry as described below. Using the experimental data, the relationship can be expressed in the form of an equation (more specifically, an equation for internal defects and an equation for external defects) using linear or non-linear fitting techniques or as a lookup table. If the relationship is expressed in the form of an equation, the degree of penetration can be computed directly by solving for the degree of penetration using the known characteristic value. If the relationship is expressed as a lookup table, the degree of penetration can be determined as the value corresponding to the determined characteristic value in the lookup table or by interpolation if the determined characteristic value does not exist in the lookup table,
The burst pressure of the casing can be calculated as a function of the outside diameter of the casing, the material strength of the casing, the wall thickness, and an applied safety factor using an equation such as Barlow's equation. The degree of penetration of the defect can be used to compute the remaining wall thickness at the defect (i.e., by subtracting the wall thickness removed as a result of the penetration of the defect from the known original wall thickness) in order to calculate the burst pressure of the casing as a result of the defect (step 630). As is known, the burst pressure provides an estimate of the fluid pressure that the casing can withstand before it ruptures. Thus, the calculated burst pressure is an important parameter to evaluate in considering the need for remedial action.
One downfall of the process 600 is that, because magnetic flux leakage is dependent upon both the degree of penetration of a defect as well as the defect's geometry, the relationship must be generated for an assumed defect geometry. For example, the relationship 700 may be constructed based upon recorded MFL signals for different degrees of penetration for a circular defect having a diameter equal to three times the thickness of the casing wall (i.e., a 3 T circular defect). Because the relationship is based upon an assumed geometry, the calculation of the degree of penetration can be incorrect if the actual defect has a geometry that deviates from the assumed geometry upon which the relationship is based. For example, the magnetic flux leakage for a 5 T circular defect is greater than the magnetic flux leakage for a 3 T circular defect of the same degree of penetration. Thus, the calculated characteristic value for a 5 T circular defect differs from the characteristic value for a 3 T circular defect of the same degree of penetration, so the calculated degree of penetration for the 5 T circular defect would differ from the actual degree of penetration due to the relationship's assumption of a 3 T circular defect.
The geometry of a defect can be estimated based upon the longitudinal range over which magnetic flux leakage is recorded (the longitudinal dimension of the defect) and the number of corrosion sensors that record the magnetic flux leakage (the azimuthal dimension of the defect). Such estimates of the defect geometry can be utilized to adjust the characteristic value to account for a difference from the assumed geometry such that the relationship can be utilized to calculate the degree of penetration. However, it can be difficult to determine the geometry of a defect from the MFL response. FIG. 8 illustrates an MFL image 800 (presented in a planar orientation rather than the cylindrical orientation in FIGS. 3 and 4) that depicts 3 T circular internal defects having degrees of penetration of 100%, 80%, 60%, and 40%. While the depictions of the defects are generally sharp in the longitudinal direction (i.e., the defect's edges are easily identifiable in the longitudinal direction), they are blurry in the azimuthal direction (i.e., the defect's edges are not easily identifiable in the azimuthal direction). The difference in resolution in the longitudinal and azimuthal directions is primarily due to the longitudinal direction of the magnetic field induced by the MFL tool. The corrosion sensors measure a sharp change in the magnetic flux at the starting and ending longitudinal positions of a defect, but corrosion sensors well outside of the azimuthal region of the defect also measure a response to the defect, which results in the blurriness of the image in the azimuthal direction. Moreover, as illustrated in FIG. 8, the blurriness is exacerbated at increased degrees of penetration. This image blurriness can make it difficult to determine the geometry of a defect. For example, it can be difficult to determine whether a defect has an azimuthal dimension of 3 T and a higher degree of penetration or an azimuthal dimension of 5 T and a lower degree of penetration. Therefore, it can be difficult to adjust the characteristic value to account for differences in geometry from the geometry upon which the relationship is based. As a result, the calculated degree of penetration and burst pressure is susceptible to errors. The invention disclosed in this application provides an improved process for determining the geometry of a defect based on MFL signals collected by a tool such as the MFL tool 100, which enables greater accuracy in the determination of the degree of penetration of the defect and the associated burst pressure of the evaluated casing.